University of Louisville

Poster Title

Development of a Fire Incidence Model to Identify Areas of High Community Risk

Institution

University of Louisville

Abstract

The primary purpose of this study was to use geographic information systems (GIS) to create a cartographic risk model to predict areas of increased potential for fire occurrences. A secondary purpose was to obtain actual fire incident data to validate the model. Census variables associated with seven risk factors for burn injury identified in the literature (categories: age, race, education level, socioeconomic status, home value, home ownership, and age of home) and GIS software were used to develop the model. Residential county fire dispatch data and statistical analysis were used to validate the model. The geographic areas identified as high and severe risk were located in the northwestern and central areas of the county. There was a strong correlation (r= 0.655) between risk model scores and actual fire incident rates. There were significant differences in mean fire rates by risk category (F = 87.58 187,3 , p < 0.001), with the exception of the low and medium risk categories. Fire incident rates among census tracts showed positive spatial autocorrelation (Moran’s I=0.542, p<0.001) and produced a map showing a significant cluster of high fire incidence in the northwestern region of the county. The fire risk model has potential to lead to more effective fire prevention and education programs. Having a model like this would allow fire departments and local governments to focus their limited resources of money, equipment, and manpower to the geographic areas that are at highest risk for fires.

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Development of a Fire Incidence Model to Identify Areas of High Community Risk

The primary purpose of this study was to use geographic information systems (GIS) to create a cartographic risk model to predict areas of increased potential for fire occurrences. A secondary purpose was to obtain actual fire incident data to validate the model. Census variables associated with seven risk factors for burn injury identified in the literature (categories: age, race, education level, socioeconomic status, home value, home ownership, and age of home) and GIS software were used to develop the model. Residential county fire dispatch data and statistical analysis were used to validate the model. The geographic areas identified as high and severe risk were located in the northwestern and central areas of the county. There was a strong correlation (r= 0.655) between risk model scores and actual fire incident rates. There were significant differences in mean fire rates by risk category (F = 87.58 187,3 , p < 0.001), with the exception of the low and medium risk categories. Fire incident rates among census tracts showed positive spatial autocorrelation (Moran’s I=0.542, p<0.001) and produced a map showing a significant cluster of high fire incidence in the northwestern region of the county. The fire risk model has potential to lead to more effective fire prevention and education programs. Having a model like this would allow fire departments and local governments to focus their limited resources of money, equipment, and manpower to the geographic areas that are at highest risk for fires.